Metal-oxide nanocrystals doped with aliovalent atoms can exhibit tunable infrared localized surface plasmon resonances (LSPRs). Yet, the range of dopant types and concentrations remains limited for many metal-oxide hosts, largely because of the difficulty in establishing reaction kinetics that favors dopant incorporation by using the co-thermolysis method. Here we develop cation-exchange reactions to introduce p-type dopants (Cu + , Ag + , etc.) into n-type metal-oxide nanocrystals, producing programmable LSPR redshifts due to dopant compensation. We further demonstrate that enhanced n-type doping can be realized via sequential cation-exchange reactions mediated by the Cu + ions. Cation-exchange transformations add a new dimension to the design of plasmonic nanocrystals, allowing preformed nanocrystals to be used as templates to create compositionally diverse nanocrystals with well-defined LSPR characteristics. The ability to tailor the doping profile postsynthetically opens the door to a multitude of opportunities to deepen our understanding of the relationship between local structure and LSPR properties.

Spontaneous adenosine release events have been discovered in the brain that last only a few seconds. The identification of these adenosine events from fast-scan cyclic voltammetry (FSCV) data is difficult due to the random nature of adenosine release. In this study, we develop an algorithm that automatically identifies and characterizes adenosine transient features, including event time, concentration, and duration. Automating the data analysis reduces analysis time from 10–18 hours to about 40 minutes per experiment. The algorithm identifies adenosine based on its two oxidation peaks, the time delay between them, and their current vs. time peak ratios. In order to validate the program, 4 data sets from 3 independent researchers were analyzed by the algorithm and then compared to manual identification by an analyst. The algorithm resulted in 10 ± 4% false negatives and 9 ± 3% false positives. The specificity of the algorithm was verified by comparing calibration data for adenosine triphosphate (ATP), histamine, hydrogen peroxide, and pH changes and these analytes were not identified as adenosine. Stimulated histamine release in vivo was also not identified as adenosine. The code is modular in design and could be easily adjusted to detect features of spontaneous dopamine or other neurochemical transients in FSCV data.

Histamine is a neurotransmitter crucial to the visual processing of Drosophila melanogaster. It is inactivated by metabolism to carcinine, a β-alanyl derivative, and the same enzyme that controls that process also converts dopamine to N-β-alanyl dopamine. Direct detection of histamine and carcinine has not been reported in single Drosophila brains. Here we quantify histamine, carcinine, dopamine, and N-β-alanyl dopamine in Drosophila tissues by capillary electrophoresis coupled to fast-scan cyclic voltammetry (CE-FSCV). Limits of detection were low, 4 ± 1 pg for histamine, 10 ± 4 pg for carcinine, 2.8 ± 0.3 pg for dopamine, and 9 ± 3 pg for N-β-alanyl-dopamine. Tissue content was compared in the brain, eyes, and cuticle from wild type (Canton S) and mutant (tan3 and ebony1) strains. In tan3 mutants, the enzyme that produces histamine from carcinine is non-functional while in ebony1 mutants, the enzyme that produces carcinine from histamine is non-functional. In all fly strains, the neurotransmitter content was highest in the eyes and there were no strain differences for tissue content in the cuticle. The main finding was that carcinine levels changed significantly in the mutant flies while histamine levels did not. In particular, tan3 flies had significantly higher carcinine levels in the eyes and brain than Canton S or ebony1 flies. N-β-alanyl-dopamine was detected in tan3 mutants, but not in other strains. These results show the utility of CE-FSCV for sensitive detection of histamine and carcinine which allows a better understanding of their content and metabolism in different types of tissues.

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Spontaneous adenosine release events have been discovered in the brain that last only a few seconds. The identification of these adenosine events from fast-scan cyclic voltammetry data has been performed manually and is difficult due to the random nature of adenosine release. In this study, we develop an algorithm that automatically identifies and characterizes adenosine transient features, including event time, concentration, and duration. Automating the data analysis reduces analysis time from 10-18 hours to about 40 minutes per experiment. The algorithm identifies adenosine based on its two oxidation peaks, the time delay between them, and their peak ratios. In order to validate the program, 4 data sets from 3 independent researchers were analyzed by the algorithm and then verified by an analyst. The algorithm resulted in 10 ± 4% false negatives and 9 ± 3% false positives. The specificity of the algorithm was verified by comparing calibration data for adenosine triphosphate, histamine, hydrogen peroxide, and pH changes and these analytes were not identified as adenosine.Stimulated histamine release in vivo was also not identified as adenosine. The code is modular in design and could be easily adjusted to detect features of spontaneous dopamine or other neurochemical transients in FSCV data.